Improving the temporal resolution of event-based electron detectors using neural network cluster analysis
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916464782475264 |
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| author | Schröder, Alexander van Velzen, Leon Kelder, Maurits Schäfer, Sascha |
| author_facet | Schröder, Alexander van Velzen, Leon Kelder, Maurits Schäfer, Sascha |
| contents | Novel event-based electron detector platforms provide an avenue to extend the temporal resolution of electron microscopy into the ultrafast domain. Here, we characterize the timing accuracy of a detector based on a TimePix3 architecture using femtosecond electron pulse trains as a reference. With a large dataset of event clusters triggered by individual incident electrons, a neural network is trained to predict the electron arrival time. Corrected timings of event clusters show a temporal resolution of 2 ns, a 1.6-fold improvement over cluster-averaged timings. This method is applicable to other fast electron detectors down to sub-nanosecond temporal resolutions, offering a promising solution to enhance the precision of electron timing for various electron microscopy applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_16666 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving the temporal resolution of event-based electron detectors using neural network cluster analysis Schröder, Alexander van Velzen, Leon Kelder, Maurits Schäfer, Sascha Instrumentation and Detectors Novel event-based electron detector platforms provide an avenue to extend the temporal resolution of electron microscopy into the ultrafast domain. Here, we characterize the timing accuracy of a detector based on a TimePix3 architecture using femtosecond electron pulse trains as a reference. With a large dataset of event clusters triggered by individual incident electrons, a neural network is trained to predict the electron arrival time. Corrected timings of event clusters show a temporal resolution of 2 ns, a 1.6-fold improvement over cluster-averaged timings. This method is applicable to other fast electron detectors down to sub-nanosecond temporal resolutions, offering a promising solution to enhance the precision of electron timing for various electron microscopy applications. |
| title | Improving the temporal resolution of event-based electron detectors using neural network cluster analysis |
| topic | Instrumentation and Detectors |
| url | https://arxiv.org/abs/2307.16666 |